Regional Relative Risk, a Physics-Based Metric for Characterizing Airborne Infectious Disease Transmission.

Airborne infectious disease transmission events occur over a wide range of spatial scales and can be an important means of disease transmission. Physics- and biology-based models can assist in predicting airborne transmission events, overall disease incidence, and disease control strategy efficacy. We describe a new theory that extends current approaches for the case in which an individual is infected by a single airborne particle, including the scenario in which numerous infectious particles are present in the air but only one causes infection. A single infectious particle can contain more than one pathogenic microorganism and be physically larger than the pathogen itself. This approach allows robust relative risk estimates even when there is wide variation in (i) individual exposures and (ii) the individual response to that exposure (the pathogen dose-response function can take any mathematical form and vary by individual). Based on this theory, we propose the regional relative risk-a new metric, distinct from the traditional relative risk metric, that compares the risk between two regions. In theory, these regions can range from individual rooms to large geographic areas. In this paper, we apply the regional relative risk metric to outdoor disease transmission events over spatial scales ranging from 50 m to 20 km, demonstrating that in many common cases minimal input information is required to use the metric. Also, we demonstrate that the model predictions are consistent with data from prior outbreaks. Future efforts could apply and validate this theory for other spatial scales, such as transmission within indoor environments. This work provides context for (i) the initial stages of an airborne disease outbreak and (ii) larger-scale disease spread, including unexpected low-probability disease "sparks" that potentially affect remote populations, a key practical issue in controlling airborne disease outbreaks. IMPORTANCE Airborne infectious disease transmission events occur over a wide range of spatial scales and can be important to disease outbreaks. We describe a new physics- and biology-based theory for the important case in which individuals are infected by a single airborne particle (even though numerous infectious particles can be emitted into the air and inhaled). Based on this theory, we propose a new epidemiological metric, regional relative risk, that compares the risk between two geographic regions (in theory, regions can range from individual rooms to large areas). Our modeling of transmission events predicts that for many scenarios of interest, minimal information is required to use this metric for locations 50 m to 20 km downwind. This prediction is consistent with data from prior disease outbreaks. Future efforts could apply and validate this theory for other spatial scales, such as indoor environments. Our results may be applicable to many airborne diseases a priori, as these results depend on the physics of airborne particulate dispersion.

Evaluation of foot and mouth disease control measures: Simulating two endemic areas of Thailand.

Foot and mouth disease (FMD) is an important livestock disease in Thailand, with outbreaks occurring every year. However, the effects of FMD control measures in Thailand have received little research attention. Epidemiological models have been widely used to evaluate FMD outbreak control, but such a model has never been developed for Thailand. We constructed a stochastic between-farm transmission model to evaluate FMD control measures. The epidemiological unit of the model was the farm, which could be in different states: susceptible, latent, undetected infectious, detected infectious and recovered. The between-farm transmission was calculated by the sum of distance-dependent transmission and trade network transmission using parameters derived from FMD outbreaks in 2016-2017. We used this model to simulate the outbreaks with and without the implementation of the following control measures: culling all animals on infected farms, ring vaccination, animal movement restrictions and isolation of infected farms. The control measures were evaluated by estimating the number of secondarily infected farms and the outbreak duration for each scenario. The model was simulated in two study areas located in the Lamphaya Klang subdistrict (high farm density) and the Bo Phloi district (low farm density). The effects of control measures differed between the two study areas. When farm density was high, rigid control measures were required to prevent a major outbreak. Among all options, culling the animals on infected farms resulted in the lowest number of infected farms and the shortest outbreak duration. In contrast, for an area with a low farm density, less stringent control measures were sufficient to control the usually minor outbreaks. The results indicate that different areas require a different approach to control an outbreak of FMD.

Pig farm vaccination against classical swine fever reduces the risk of transmission from wild boar.

In 2018, classical swine fever (CSF) re-emerged in the Gifu Prefecture, central Japan, causing an on-going outbreak among wild boars and domestic pigs in the country. Consequently, oral vaccination for wild boar and compulsory vaccination for pig farms started in 2019. We have previously shown that, before vaccination in the Gifu Prefecture, the presence of CSF-infected wild boar near pig farms increased the risk of CSF transmission. This study aimed to re-evaluate the transmission risk from wild boars to pig farms under a vaccination program. The effectiveness of vaccination was evaluated by comparing the transmission risk estimated before and after the implementation of vaccinations. In this study, we focused on two affected areas, the Kanto (eastern Japan) and Kinki (west-central Japan) regions, in which eight of 11 infected farms were detected between the start of pig farm vaccinations and April 2021. Wild boar surveillance data from an area within a 50-km radius from the infected farms were used for analysis, consisting of 18,870 1-km grid cells (207 infected cells) in the Kanto region, and 15,677 cells (417 infected cells) in the Kinki region. The transmission rates in the post-vaccination period in the Kanto and Kinki regions were much lower than that in the pre-vaccination period in the Gifu Prefecture. The values of transmission kernels (h0, transmission rate at 0 km) in the Kanto and Kinki regions decreased to 1% of the transmission kernel in the pre-vaccination period. In the pre-vaccination period, the risk of infection within 300 days was almost 95 % when one infected grid cell was detected within 1 km of a pig farm. Meanwhile, in the post-vaccination period, the risk of infection within 300 days was approximately 5% when several infected cells were detected within 1 km of a pig farm. Considering the limited effect of oral vaccination for wild boar due to distribution limitations in the Kanto and Kinki regions, vaccination on pig farms may seems to have mainly reduced the transmission risk from wild boar. However, despite the implementation of vaccination, the risk of infection on pig farms remains present due to the immunity gap of weaning pigs. Therefore, strict biosecurity measures on pig farms and an appropriate vaccination program are required to prevent and control CSF spread.

Social structure defines spatial transmission of African swine fever in wild boar.

The spatial spread of infectious disease is determined by spatial and social processes such as animal space use and family group structure. Yet, the impacts of social processes on spatial spread remain poorly understood and estimates of spatial transmission kernels (STKs) often exclude social structure. Understanding the impacts of social structure on STKs is important for obtaining robust inferences for policy decisions and optimizing response plans. We fit spatially explicit transmission models with different assumptions about contact structure to African swine fever virus surveillance data from eastern Poland from 2014 to 2015 and evaluated how social structure affected inference of STKs and spatial spread. The model with social structure provided better inference of spatial spread, predicted that approximately 80% of transmission events occurred within family groups, and that transmission was weakly female-biased (other models predicted weakly male-biased transmission). In all models, most transmission events were within 1.5 km, with some rare events at longer distances. Effective reproductive numbers were between 1.1 and 2.5 (maximum values between 4 and 8). Social structure can modify spatial transmission dynamics. Accounting for this additional contact heterogeneity in spatial transmission models could provide more robust inferences of STKs for policy decisions, identify best control targets and improve transparency in model uncertainty.

Elucidating the Local Transmission Dynamics of Highly Pathogenic Avian Influenza H5N6 in the Republic of Korea by Integrating Phylogenetic Information.

Highly pathogenic avian influenza (HPAI) virus is one of the most virulent and infectious pathogens of poultry. As a response to HPAI epidemics, veterinary authorities implement preemptive depopulation as a controlling strategy. However, mass culling within a uniform radius of the infection site can result in unnecessary depopulation. Therefore, it is useful to quantify the transmission distance from infected premises (IPs) before determining the optimal area for preemptive depopulation. Accordingly, we analyzed the transmission risk within spatiotemporal clusters of IPs using transmission kernel estimates derived from phylogenetic clustering information on 311 HPAI H5N6 IPs identified during the 2016-2017 epidemic, Republic of Korea. Subsequently, we explored the impact of varying the culling radius on the local transmission of HPAI given the transmission risk estimates. The domestic duck farm density was positively associated with higher transmissibility. Ring culling over a radius of 3 km may be effective for areas with high dense duck holdings, but this approach does not appear to significantly reduce the risk for local transmission in areas with chicken farms. This study provides the first estimation of the local transmission dynamics of HPAI in the Republic of Korea as well as insight into determining an effective ring culling radius.

Estimation of infection risk on pig farms in infected wild boar areas-Epidemiological analysis for the reemergence of classical swine fever in Japan in 2018.

In September 2018, classical swine fever (CSF) reemerged in Japan after 26 years' absence. The first case was detected at a pig farm in Gifu Prefecture, in the center of Japan, and the disease spread to both domestic pigs and wild boar (Sus scrofa). The spread of CSF in wild boar is extremely difficult to control and is thus a great threat to domestic pig farms, and understanding the transmission risk from wild boar to domestic pigs is essential to implement effective control measures that will prevent domestic pig infection. Therefore, this study elucidates the transmission risk from wild boar to domestic pigs by introducing a transmission kernel that is dependent on the distance between infected wild boar and pig farms, and then estimating the risk area of infection from wild boar by describing the transmission probability. The study used epidemiological data from Gifu Prefecture in the period from September 2018 to March 2019, including a total of 171 1-km grid cells where an infected wild boar was detected and pig farm data from 13 infected and 34 uninfected farms. The estimated infection risk area within 28 days matched well with the observed data. The risk area widened gradually during the epidemic, and at the end of March, the risk area extended over a range of approximately 75 km from east to west and 40 km from north to south (almost 3000 km2). Ten out of the 13 infected farms and four out of the 34 uninfected farms were located within the high-risk area (>60 % infection probability). In contrast, one infected farm and 18 uninfected farms were located within the low-risk area (<5 % infection probability). When several infected grid cells were detected within 5 km of a pig farm, the risk of infection from wild boar within 28 days was more than 5 %. This analysis provides an estimate of the potential spatial range over which CSF virus can spread between wild boar and domestic pig farms, and can be used to inform the early detection of CSF-suspected pigs and the strengthening of biosecurity measures that will effectively prevent and control the disease based on the infection risk level.

Identification of High-Risk Areas for the Spread of Highly Pathogenic Avian Influenza in Central Luzon, Philippines.

Highly pathogenic avian influenza virus (HPAIV) is a major problem in the poultry industry. It is highly contagious and is associated with a high mortality rate. The Philippines experienced an outbreak of avian influenza (AI) in 2017. As there is always a risk of re-emergence, efforts to manage disease outbreaks should be optimal. Linked to this is the need for an effective surveillance procedure to capture disease outbreaks at their early stage. Risk-based surveillance is the most effective and economical approach to outbreak management. This study evaluated the potential of commercial poultry farms in Central Luzon to transmit HPAI by calculating their respective reproductive ratios (R0). The reproductive number for each farm is based on the spatial kernel and the infectious period. A risk map has been created based on the calculated R0. There were 882 (76.63%) farms with R0 < 1. Farms with R0 ≥ 1 were all located in Pampanga Province. These farms were concentrated in the towns of San Luis (n = 12) and Candaba (n = 257). This study demonstrates the utility of mapping farm-level R0 estimates for informing HPAI risk management activities.

Modeling the role of carrier and mobile herds on foot-and-mouth disease virus endemicity in the Far North Region of Cameroon.

Foot and mouth disease virus (FMDV) is an RNA virus that infects cloven-hoofed animals, often produces either epidemic or endemic conditions, and negatively affects agricultural economies worldwide. FMDV epidemic dynamics have been extensively studied, but understanding of drivers of disease persistence in areas in which FMDV is endemic, such as most of sub-Saharan Africa, is lacking. We present a spatial stochastic model of disease dynamics that incorporates a spatial transmission kernel in a modified Gillespie algorithm, and use it to evaluate two hypothesized drivers of endemicity: asymptomatic carriers and the movement of mobile herds. The model is parameterized using data from the pastoral systems in the Far North Region of Cameroon. Our computational study provides evidence in support of the hypothesis that asymptomatic carriers, but not mobile herds, are a driver of endemicity.

Mathematical model of the 2010 foot-and-mouth disease epidemic in Japan and evaluation of control measures.

A large-scale foot-and-mouth disease (FMD) epidemic occurred in Japan in 2010. The epidemic arose in an area densely populated with cattle and pigs, continued for 3 months, and was contained by emergency vaccination. In this study, a mathematical simulation model of FMD transmission between farms was developed to generate the disease spread in the affected area. First, a farm-distance-based transmission kernel was estimated using the epidemic data. The estimated transmission kernel was then incorporated into the transmission model to evaluate the effectiveness of several control measures. The baseline model provided a good fit to the observed data during the period from imposition of movement restrictions until the implementation of vaccination. Our simulation results demonstrated that prompt culling on infected farms after detection could contribute to reducing the disease spread. The number of infected farms decreased to 30% of the baseline model by applying the 24-h prompt culling scenario. The early detection scenario resulted in a smaller-sized epidemic. However, the results of this scenario included a 35% chance of large-scale epidemic (more than 500 infected farms), even when the disease was detected 14 days earlier than in the baseline model. As additional options, preemptive culling could halt the epidemic more effectively. However, the preemptive culling scenario required substantial resources for culling operations. The 1-km preemptive scenario involved more than 50 farms remaining to be culled per day. Therefore, preemptive culling scenarios accompanied some difficulties in maintaining a sufficient capacity for culling in the affected area. A 10-km vaccination 7 days after the first detection of the disease demonstrated the potential to contain the epidemic to a small scale, while implementation of a 3-km vaccination on the same day could not effectively reduce epidemic size. In vaccination scenarios, the total number of farms that were either culled or vaccinated exceeded that of the baseline model. Vaccination scenarios therefore posed a problem of appropriate management of many vaccinated animals, whether these vaccinated animals would be culled or not. The present FMD transmission model developed using the 2010 FMD epidemic data in Japan provides useful information for consideration of suitable control strategies against FMD.